A total of 864 papers were identified from MEDLINE, EMBASE, HEED, NHS EED and HTA databases. The CEAR returned seven results (three of which were also identified in the MEDLINE and EMBASE search) and the EuroQol website identified eight studies (none of which were identified in the MEDLINE and EMBASE search). Four additional relevant publications were identified by supplementary citation searching (Fig. 1).
Figure 1. Search results. *Currently, there are no validated methods or filters for searching for the type of QoL data that are required for decision analytic models . The search strategy used in this study was found to be reasonably precise within MEDLINE and EMBASE, returning a manageable number of references and omitting few relevant records. The filters used for the MEDLINE & EMBASE and HEED searches, developed and used by the National Clinical Guideline Centre, are available as online appendices. †Of those that included relevant health states, 11 measured quality of life using disease-specific instruments such as the IPSS; Acute Physiology and Chronic Health Evaluation scores; Functional Assessment of Chronic Illness Therapy, and rank ordering of different conditions, among others.
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Twelve studies (reported in 16 separate papers) met our inclusion criteria. With the exception of two papers [9,10] that were identified through citation searching and the CEAR, respectively, all were retrieved through MEDLINE and EMBASE.
Given the heterogeneity between studies in terms of patient characteristics and QoL assessment methods, there was no attempt to pool results. Instead, the population, methods and results of each study are reported below. More detailed reports of studies using preference-based measures and non-preference based measures with mapped estimates are shown in Table 1[11–23].
Table 1. Reported and mapped generic preference-based utility values for patients with a symptomatic UTI
The search did not identify any primary studies of QoL in patients with UTI-associated bacteraemia. Several studies contained utility values for sepsis; however, the infections were not of urinary tract origin and were thought to describe a more severe health state than the one under review.
QUALITY OF LIFE IN PATIENTS WITH A UTI
As previously discussed, Barry et al.  estimated a monthly disutility of 0.2894 for persistent dysuria and a disutility of 0.3732 for patients with pylonephritis, using the IWB. Ackerman et al.  reported utility values in 13 men with moderate to severe BPH. A series of BPH-specific health states were described according to three treatments, five short-term clinical events, and 17 possible long-term outcomes. To assign preference weights to each health state, the standard gamble was administered by a trained interviewer. Results were reported according to patients’ risk attitudes. Risk-averse individuals (n= 6) reported a mean utility value of 97.2 (se 1.1; range 94–99) for severe UTIs, while non-risk-averse patients (n= 7) reported a mean value of 89.3 (se 4.6; range 77–99).
In 1998, Gold et al.  published a catalogue of 130 health state values developed using the Health and Activity Limitation Index (HALex), derived from the answers to two questions asked in the US National Health Interview Survey about activity limitations and self-rated health. Between 1987 and 1992, 84 443 people were included in the survey; at the time of each survey, a total of 384 people reported having a bladder infection and 387 reported having a kidney infection. Based on weights developed from a correspondence analysis and multi-attribute utility model, bladder infections were assigned a mean QoL value of 0.73 (median 0.84; interquartile range [IQR] 0.4) and kidney infection a value of 0.66 (median 0.63; IQR 0.36).
Unable to find relevant utility data for patients with acute pylonephritis, Yen et al.  asked a panel of six emergency physicians and internists to develop utility weights using the standard reference gamble technique. Pylonephritis was assigned a QALY of 0.90, 0.87 for pylonephritis with mild side effects, and 0.81 for pylonephritis with serious side effects.
Sonnenberg et al.  reported the utility associated with UTI from ‘a sample of female members of the research team and advisor panel’ using the time trade-off technique. They report a short-term disutility of 0.0192 for UTI. Similarly, Lawler et al.  used their judgement to arrive at an estimated utility value of 0.99 for people suffering from UTI.
Three studies measured the impact of UTI on QoL among otherwise healthy adult women. In 2000, Ellis and Verma  conducted a case–control study to evaluate the effect of UTI on QoL in women using the SF-36. Although the authors mentioned that QoL was lower in patients with severe UTI, these results were not reported. The authors of this study were contacted for further information; a reply was received but additional data was not available. The algorithm published by Ara and Brazier  was used to map mean SF-36 scores to EQ-5D health state values (Table 1).
Ernst et al.  evaluated QoL among women with acute cystitis and the impact of treatment on QoL. Patients were randomized to receive either trimethoprim/sulfamethoxazole for 3 days or nitrofuratonin for 7 days. The Quality of Well Being (QWB) questionnaire was administered at baseline and 3, 7, 14, and 28 days after the initial visit. The QWB value at baseline (i.e. having a UTI) was 0.68 (sd 0.03) and 0.81 (sd 0.11) at the 28-day follow-up. Patients who experienced clinical cure had significantly better QoL scores at days 3 (0.77 vs 0.72), 7 (0.82 vs 0.71) and 14 (0.83 vs 0.76) compared with those who failed treatment.
Abrahamian et al.  measured QoL in women with UTIs caused by TMP/SMX-resistant isolates compared with those with susceptible isolates and evaluated the effect of treatment failure on QoL. At initial presentation, patients were treated with TMP/SMX twice daily for 3 days and phenazopyridine hydrochloride three times a day for 2 days. The SF-36 was administered 3–7 days after treatment and results were reported according to whether the patient had been infected with TMP/SMX-susceptible or -resistant strains and whether they had experienced clinical cure or failure. Results were presented alongside US national mean scores for women aged 25–34 years. For the purpose of the present analysis, mean SF-36 dimension scores were mapped to EQ-5D health state values using the algorithm described by Ara and Brazier  (Table 1).
Maxwell et al.  measured QoL in older adults living in care homes using the Health Utilities Index Mark 2 (HUI2). Results were reported according to the presence or absence of several different clinical conditions, including UTI. The HUI2 was scored according to the published Canadian preference weights.
Two different research groups have used the SF questionnaires to evaluate the effect of UTIs on individuals with spinal cord injury (SCI). Haran et al.  and Lee et al. [19,27] have published a series of articles reporting the use of the SF-36 in individuals with SCI. Haran et al.  specified that individuals with a UTI have worse general health, vitality, and mental health domain scores than those who do not have a UTI, but did not report specific domain values for these groups. This paper cites a website containing SF-36 data stratified by age, sex and impairment group, but at the time of writing this link was not functional. The authors were contacted but were unable to provide additional information. In 2008, the group published mapped SF-6D values derived from both the full SF-36 and the recalculated SF-12 scores .
A long-term cohort study of individuals with SCI by Vogel et al.  reported a statistically significant difference in SF-12 scores for patients with a UTI or severe UTI compared with patients without a UTI; however, the SF-12 values were not reported. Upon request, the authors provided us with anonymized patient-level SF-12 responses from their most recent follow-up [16,17]. Five of the 415 cases contained missing data; they were assumed to be missing completely at random and were omitted from the analysis. Using an algorithm developed by Gray et al.  and the accompanying spreadsheet available on the Health Economics Research Centre website , EQ-5D values were estimated. Because the Gray algorithm contains random number generators, it was necessary to run a simulation (10 000 times) in order to obtain mean EQ-5D estimates for each health state. All calculations were performed using Microsoft Excel 2007. The results of the mapping, as well as the physical and mental component summary scores provided by the authors are shown in Table 1.